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Creators/Authors contains: "Jennings, Emily E."

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  1. Abstract

    Synthetic approaches based on the patterned deposition of volatile molecules from the vapor phase are used extensively in the creation of surface‐chemical gradients; however, the ability to generate diffusion‐controlled 1D and 2D gradients from multiple sources remains a challenge. The current work reports a one‐step approach to the synthesis of continuous and periodic chemical gradients with simple and intricate geometries using multiple sources within custom reaction chambers. Specifically, this approach provides precise, simultaneous control over the physicochemical conditions (e.g., concentration, evaporation rate, and direction of diffusion flux of the chemical moieties) and the geometrical parameters (e.g., size, shape, and position) during surface functionalization, thus enabling materials with predictable surface‐chemical gradients applicable to the manipulation and/or organization of liquid droplets and that can generate assemblies of functional solids (e.g., silver nanoparticles) that are transferrable via stamping. These surfaces can be useful to various fields, for example, molecular diagnostics and microfabrication. Furthermore, this work extends the application of these surfaces to the precise placement and manipulation of gas bubbles that can have potential use in, for example, controlling bubble nucleation in processes designed to manage heat transfer.

     
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  2. Premise

    Leaf morphology is dynamic, continuously deforming during leaf expansion and among leaves within a shoot. Here, we measured the leaf morphology of more than 200 grapevines (Vitisspp.) over four years and modeled changes in leaf shape along the shoot to determine whether a composite leaf shape comprising all the leaves from a single shoot can better capture the variation and predict species identity compared with individual leaves.

    Methods

    Using homologous universal landmarks found in grapevine leaves, we modeled various morphological features as polynomial functions of leaf nodes. The resulting functions were used to reconstruct modeled leaf shapes across the shoots, generating composite leaves that comprehensively capture the spectrum of leaf morphologies present.

    Results

    We found that composite leaves are better predictors of species identity than individual leaves from the same plant. We were able to use composite leaves to predict the species identity of previously unassigned grapevines, which were verified with genotyping.

    Discussion

    Observations of individual leaf shape fail to capture the true diversity between species. Composite leaf shape—an assemblage of modeled leaf snapshots across the shoot—is a better representation of the dynamic and essential shapes of leaves, in addition to serving as a better predictor of species identity than individual leaves.

     
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